Income

Atwater Village Income

## Will start with importing data
import pandas as pd

# We will begin by loading a data file for Atwater Village that details the structures built in the area.
# note the relative filepath! where is this file located?
df = pd.read_csv('data/Atwater2018_HHIncomeCategories.csv')

df = pd.read_csv(
    'data/Atwater2018_HHIncomeCategories.csv',
    dtype=
    {
        'Geo_FIPS':str,
        'Geo_STATE':str,
        'Geo_COUNTY': str
    }
)

df.columns[df.isna().all()].tolist()

['Geo_US',
 'Geo_REGION',
 'Geo_DIVISION',
 'Geo_STATECE',
 'Geo_COUSUB',
 'Geo_PLACE',
 'Geo_PLACESE',
 'Geo_CONCIT',
 'Geo_AIANHH',
 'Geo_AIANHHFP',
 'Geo_AIHHTLI',
 'Geo_AITSCE',
 'Geo_AITS',
 'Geo_ANRC',
 'Geo_CBSA',
 'Geo_CSA',
 'Geo_METDIV',
 'Geo_MACC',
 'Geo_MEMI',
 'Geo_NECTA',
 'Geo_CNECTA',
 'Geo_NECTADIV',
 'Geo_UA',
 'Geo_UACP',
 'Geo_CDCURR',
 'Geo_SLDU',
 'Geo_SLDL',
 'Geo_VTD',
 'Geo_ZCTA3',
 'Geo_ZCTA5',
 'Geo_SUBMCD',
 'Geo_SDELM',
 'Geo_SDSEC',
 'Geo_SDUNI',
 'Geo_UR',
 'Geo_PCI',
 'Geo_TAZ',
 'Geo_UGA',
 'Geo_BTTR',
 'Geo_BTBG',
 'Geo_PUMA5',
 'Geo_PUMA1']

df = df.dropna(axis=1,how="all")

df.head()

	Geo_FIPS	Geo_GEOID	Geo_NAME	Geo_QName	Geo_STUSAB	Geo_SUMLEV	Geo_GEOCOMP	Geo_FILEID	Geo_LOGRECNO	Geo_STATE	Geo_COUNTY	Geo_TRACT	Geo_BLKGRP	SE_B14001_001	SE_B14001_002	SE_B14001_003	SE_B14001_004	SE_B14001_005	SE_B14001_006
0	060371863011	15000US060371863011	Block Group 1	Block Group 1, Census Tract 1863.01, Los Angel...	ca	150	0	ACSSF	15233	06	037	186301	1	296	39	40	71	14	132
1	060371863012	15000US060371863012	Block Group 2	Block Group 2, Census Tract 1863.01, Los Angel...	ca	150	0	ACSSF	15234	06	037	186301	2	286	79	112	38	10	47
2	060371863013	15000US060371863013	Block Group 3	Block Group 3, Census Tract 1863.01, Los Angel...	ca	150	0	ACSSF	15235	06	037	186301	3	308	81	124	55	25	23
3	060371863021	15000US060371863021	Block Group 1	Block Group 1, Census Tract 1863.02, Los Angel...	ca	150	0	ACSSF	15236	06	037	186302	1	578	129	83	112	92	162
4	060371864011	15000US060371864011	Block Group 1	Block Group 1, Census Tract 1864.01, Los Angel...	ca	150	0	ACSSF	15239	06	037	186401	1	655	178	151	163	72	91

# list of additional columns to drop
columns_to_drop = ['Geo_GEOID','Geo_STUSAB','Geo_SUMLEV','Geo_GEOCOMP','Geo_FILEID','Geo_LOGRECNO','Geo_STATE','Geo_COUNTY','Geo_TRACT']

# next, drop them!
df = df.drop(columns_to_drop,axis=1)
df.head()

	Geo_FIPS	Geo_NAME	Geo_QName	Geo_BLKGRP	SE_B14001_001	SE_B14001_002	SE_B14001_003	SE_B14001_004	SE_B14001_005	SE_B14001_006
0	060371863011	Block Group 1	Block Group 1, Census Tract 1863.01, Los Angel...	1	296	39	40	71	14	132
1	060371863012	Block Group 2	Block Group 2, Census Tract 1863.01, Los Angel...	2	286	79	112	38	10	47
2	060371863013	Block Group 3	Block Group 3, Census Tract 1863.01, Los Angel...	3	308	81	124	55	25	23
3	060371863021	Block Group 1	Block Group 1, Census Tract 1863.02, Los Angel...	1	578	129	83	112	92	162
4	060371864011	Block Group 1	Block Group 1, Census Tract 1864.01, Los Angel...	1	655	178	151	163	72	91

df.columns = ['FIPS',
'Geo_NAME',
 'Geo_QName',
 'Geo_BLKGRP',
 'Total Households in Atwater Village',
 'Household Income Less than $25,000',
 'Household Income $25,000 to $49,999',
 'Household Income $50,000 to $74,999',
 'Household Income $75,000 to $99,999',
 'Household Income $100,000 or More']

df.head()

	FIPS	Geo_NAME	Geo_QName	Geo_BLKGRP	Total Households in Atwater Village	Household Income Less than $25,000	Household Income $25,000 to $49,999	Household Income $50,000 to $74,999	Household Income $75,000 to $99,999	Household Income $100,000 or More
0	060371863011	Block Group 1	Block Group 1, Census Tract 1863.01, Los Angel...	1	296	39	40	71	14	132
1	060371863012	Block Group 2	Block Group 2, Census Tract 1863.01, Los Angel...	2	286	79	112	38	10	47
2	060371863013	Block Group 3	Block Group 3, Census Tract 1863.01, Los Angel...	3	308	81	124	55	25	23
3	060371863021	Block Group 1	Block Group 1, Census Tract 1863.02, Los Angel...	1	578	129	83	112	92	162
4	060371864011	Block Group 1	Block Group 1, Census Tract 1864.01, Los Angel...	1	655	178	151	163	72	91

df['Total Households in Atwater Village'].head()

0    296
1    286
2    308
3    578
4    655
Name: Total Households in Atwater Village, dtype: int64

df.head()

	FIPS	Geo_NAME	Geo_QName	Geo_BLKGRP	Total Households in Atwater Village	Household Income Less than $25,000	Household Income $25,000 to $49,999	Household Income $50,000 to $74,999	Household Income $75,000 to $99,999	Household Income $100,000 or More
0	060371863011	Block Group 1	Block Group 1, Census Tract 1863.01, Los Angel...	1	296	39	40	71	14	132
1	060371863012	Block Group 2	Block Group 2, Census Tract 1863.01, Los Angel...	2	286	79	112	38	10	47
2	060371863013	Block Group 3	Block Group 3, Census Tract 1863.01, Los Angel...	3	308	81	124	55	25	23
3	060371863021	Block Group 1	Block Group 1, Census Tract 1863.02, Los Angel...	1	578	129	83	112	92	162
4	060371864011	Block Group 1	Block Group 1, Census Tract 1864.01, Los Angel...	1	655	178	151	163	72	91

Clean Up Data for Bar Graphs (Atwater)

df_sum= df.sum().reset_index()

df_sum=df_sum.set_index('index').T

df_sum.head()

index	FIPS	Geo_NAME	Geo_QName	Geo_BLKGRP	Total Households in Atwater Village	Household Income Less than $25,000	Household Income $25,000 to $49,999	Household Income $50,000 to $74,999	Household Income $75,000 to $99,999	Household Income $100,000 or More
0	0603718630110603718630120603718630130603718630...	Block Group 1Block Group 2Block Group 3Block G...	Block Group 1, Census Tract 1863.01, Los Angel...	31	8801	1982	1644	1413	1028	2734

df_sum['% Less than $25K'] = df_sum['Household Income Less than $25,000']/df_sum['Total Households in Atwater Village']*100

df_sum['% More than $100K'] = df_sum['Household Income $100,000 or More']/df_sum['Total Households in Atwater Village']*100

df_sum['neighborhood'] = 'Atwater'
df_sum['year'] = '2018'

df_sum.plot.bar(x = 'neighborhood', y='% Less than $25K', title="Percentage of Households with Income Less than $25K (2018)")

<matplotlib.axes._subplots.AxesSubplot at 0x7fa82d552100>

df_sum.plot.bar(x = 'neighborhood', y='% More than $100K', title="Percentage of Households with Income More than $100K (2018)")

<matplotlib.axes._subplots.AxesSubplot at 0x7fa82b4cb4f0>

df_sum.head()

index	FIPS	Geo_NAME	Geo_QName	Geo_BLKGRP	Total Households in Atwater Village	Household Income Less than $25,000	Household Income $25,000 to $49,999	Household Income $50,000 to $74,999	Household Income $75,000 to $99,999	Household Income $100,000 or More	% Less than $25K	% More than $100K	neighborhood	year
0	0603718630110603718630120603718630130603718630...	Block Group 1Block Group 2Block Group 3Block G...	Block Group 1, Census Tract 1863.01, Los Angel...	31	8801	1982	1644	1413	1028	2734	22.5202	31.0647	Atwater	2018

Normalize Data for Mapping

df['neighborhood'] = 'Atwater'

df['p_less_25k'] = df['Household Income Less than $25,000']/df['Total Households in Atwater Village']*100

df['p_more_100k'] = df['Household Income $100,000 or More']/df['Total Households in Atwater Village']*100

df['Total Households'] = df ['Total Households in Atwater Village']

df = df.dropna(axis=1,how="all")

df.head()

	FIPS	Geo_NAME	Geo_QName	Geo_BLKGRP	Total Households in Atwater Village	Household Income Less than $25,000	Household Income $25,000 to $49,999	Household Income $50,000 to $74,999	Household Income $75,000 to $99,999	Household Income $100,000 or More	neighborhood	p_less_25k	p_more_100k	Total Households
0	060371863011	Block Group 1	Block Group 1, Census Tract 1863.01, Los Angel...	1	296	39	40	71	14	132	Atwater	13.175676	44.594595	296
1	060371863012	Block Group 2	Block Group 2, Census Tract 1863.01, Los Angel...	2	286	79	112	38	10	47	Atwater	27.622378	16.433566	286
2	060371863013	Block Group 3	Block Group 3, Census Tract 1863.01, Los Angel...	3	308	81	124	55	25	23	Atwater	26.298701	7.467532	308
3	060371863021	Block Group 1	Block Group 1, Census Tract 1863.02, Los Angel...	1	578	129	83	112	92	162	Atwater	22.318339	28.027682	578
4	060371864011	Block Group 1	Block Group 1, Census Tract 1864.01, Los Angel...	1	655	178	151	163	72	91	Atwater	27.175573	13.893130	655

import plotly.express as px
import pandas as pd

Leimert Park Income

df2 = pd.read_csv('data/Leimert2018_HHIncomeCategories.csv')

df2 = pd.read_csv(
    'data/Leimert2018_HHIncomeCategories.csv',
    dtype=
    {
        'Geo_FIPS':str,
        'Geo_STATE':str,
        'Geo_COUNTY': str
    }
)

df2.head()

	Geo_FIPS	Geo_GEOID	Geo_NAME	Geo_QName	Geo_STUSAB	Geo_SUMLEV	Geo_GEOCOMP	Geo_FILEID	Geo_LOGRECNO	Geo_US	...	Geo_BTTR	Geo_BTBG	Geo_PUMA5	Geo_PUMA1	SE_B14001_001	SE_B14001_002	SE_B14001_003	SE_B14001_004	SE_B14001_005	SE_B14001_006
0	060372190203	15000US060372190203	Block Group 3	Block Group 3, Census Tract 2190.20, Los Angel...	ca	150	0	ACSSF	15884	NaN	...	NaN	NaN	NaN	NaN	323	103	35	107	51	27
1	060372340001	15000US060372340001	Block Group 1	Block Group 1, Census Tract 2340, Los Angeles ...	ca	150	0	ACSSF	16105	NaN	...	NaN	NaN	NaN	NaN	628	181	149	112	36	150
2	060372340002	15000US060372340002	Block Group 2	Block Group 2, Census Tract 2340, Los Angeles ...	ca	150	0	ACSSF	16106	NaN	...	NaN	NaN	NaN	NaN	260	14	61	29	11	145
3	060372340003	15000US060372340003	Block Group 3	Block Group 3, Census Tract 2340, Los Angeles ...	ca	150	0	ACSSF	16107	NaN	...	NaN	NaN	NaN	NaN	299	18	87	25	29	140
4	060372340004	15000US060372340004	Block Group 4	Block Group 4, Census Tract 2340, Los Angeles ...	ca	150	0	ACSSF	16108	NaN	...	NaN	NaN	NaN	NaN	580	201	32	138	110	99

5 rows × 61 columns

df2.columns[df2.isna().all()].tolist()

['Geo_US',
 'Geo_REGION',
 'Geo_DIVISION',
 'Geo_STATECE',
 'Geo_COUSUB',
 'Geo_PLACE',
 'Geo_PLACESE',
 'Geo_CONCIT',
 'Geo_AIANHH',
 'Geo_AIANHHFP',
 'Geo_AIHHTLI',
 'Geo_AITSCE',
 'Geo_AITS',
 'Geo_ANRC',
 'Geo_CBSA',
 'Geo_CSA',
 'Geo_METDIV',
 'Geo_MACC',
 'Geo_MEMI',
 'Geo_NECTA',
 'Geo_CNECTA',
 'Geo_NECTADIV',
 'Geo_UA',
 'Geo_UACP',
 'Geo_CDCURR',
 'Geo_SLDU',
 'Geo_SLDL',
 'Geo_VTD',
 'Geo_ZCTA3',
 'Geo_ZCTA5',
 'Geo_SUBMCD',
 'Geo_SDELM',
 'Geo_SDSEC',
 'Geo_SDUNI',
 'Geo_UR',
 'Geo_PCI',
 'Geo_TAZ',
 'Geo_UGA',
 'Geo_BTTR',
 'Geo_BTBG',
 'Geo_PUMA5',
 'Geo_PUMA1']

df2 = df2.dropna(axis=1,how="all")

df2.head()

	Geo_FIPS	Geo_GEOID	Geo_NAME	Geo_QName	Geo_STUSAB	Geo_SUMLEV	Geo_GEOCOMP	Geo_FILEID	Geo_LOGRECNO	Geo_STATE	Geo_COUNTY	Geo_TRACT	Geo_BLKGRP	SE_B14001_001	SE_B14001_002	SE_B14001_003	SE_B14001_004	SE_B14001_005	SE_B14001_006
0	060372190203	15000US060372190203	Block Group 3	Block Group 3, Census Tract 2190.20, Los Angel...	ca	150	0	ACSSF	15884	06	037	219020	3	323	103	35	107	51	27
1	060372340001	15000US060372340001	Block Group 1	Block Group 1, Census Tract 2340, Los Angeles ...	ca	150	0	ACSSF	16105	06	037	234000	1	628	181	149	112	36	150
2	060372340002	15000US060372340002	Block Group 2	Block Group 2, Census Tract 2340, Los Angeles ...	ca	150	0	ACSSF	16106	06	037	234000	2	260	14	61	29	11	145
3	060372340003	15000US060372340003	Block Group 3	Block Group 3, Census Tract 2340, Los Angeles ...	ca	150	0	ACSSF	16107	06	037	234000	3	299	18	87	25	29	140
4	060372340004	15000US060372340004	Block Group 4	Block Group 4, Census Tract 2340, Los Angeles ...	ca	150	0	ACSSF	16108	06	037	234000	4	580	201	32	138	110	99

columns_to_drop = ['Geo_GEOID','Geo_STUSAB','Geo_SUMLEV','Geo_GEOCOMP','Geo_FILEID','Geo_LOGRECNO','Geo_STATE','Geo_COUNTY','Geo_TRACT']

df2.head()

	Geo_FIPS	Geo_GEOID	Geo_NAME	Geo_QName	Geo_STUSAB	Geo_SUMLEV	Geo_GEOCOMP	Geo_FILEID	Geo_LOGRECNO	Geo_STATE	Geo_COUNTY	Geo_TRACT	Geo_BLKGRP	SE_B14001_001	SE_B14001_002	SE_B14001_003	SE_B14001_004	SE_B14001_005	SE_B14001_006
0	060372190203	15000US060372190203	Block Group 3	Block Group 3, Census Tract 2190.20, Los Angel...	ca	150	0	ACSSF	15884	06	037	219020	3	323	103	35	107	51	27
1	060372340001	15000US060372340001	Block Group 1	Block Group 1, Census Tract 2340, Los Angeles ...	ca	150	0	ACSSF	16105	06	037	234000	1	628	181	149	112	36	150
2	060372340002	15000US060372340002	Block Group 2	Block Group 2, Census Tract 2340, Los Angeles ...	ca	150	0	ACSSF	16106	06	037	234000	2	260	14	61	29	11	145
3	060372340003	15000US060372340003	Block Group 3	Block Group 3, Census Tract 2340, Los Angeles ...	ca	150	0	ACSSF	16107	06	037	234000	3	299	18	87	25	29	140
4	060372340004	15000US060372340004	Block Group 4	Block Group 4, Census Tract 2340, Los Angeles ...	ca	150	0	ACSSF	16108	06	037	234000	4	580	201	32	138	110	99

df2 = df2.drop(columns_to_drop,axis=1)

df2.head()

	Geo_FIPS	Geo_NAME	Geo_QName	Geo_BLKGRP	SE_B14001_001	SE_B14001_002	SE_B14001_003	SE_B14001_004	SE_B14001_005	SE_B14001_006
0	060372190203	Block Group 3	Block Group 3, Census Tract 2190.20, Los Angel...	3	323	103	35	107	51	27
1	060372340001	Block Group 1	Block Group 1, Census Tract 2340, Los Angeles ...	1	628	181	149	112	36	150
2	060372340002	Block Group 2	Block Group 2, Census Tract 2340, Los Angeles ...	2	260	14	61	29	11	145
3	060372340003	Block Group 3	Block Group 3, Census Tract 2340, Los Angeles ...	3	299	18	87	25	29	140
4	060372340004	Block Group 4	Block Group 4, Census Tract 2340, Los Angeles ...	4	580	201	32	138	110	99

df2.columns = ['FIPS',
'Geo_NAME',
 'Geo_QName',
 'Geo_BLKGRP',
 'Total Households in Leimert Park',
 'Household Income Less than $25,000',
 'Household Income $25,000 to $49,999',
 'Household Income $50,000 to $74,999',
 'Household Income $75,000 to $99,999',
 'Household Income $100,000 or More']

df2.head()

	FIPS	Geo_NAME	Geo_QName	Geo_BLKGRP	Total Households in Leimert Park	Household Income Less than $25,000	Household Income $25,000 to $49,999	Household Income $50,000 to $74,999	Household Income $75,000 to $99,999	Household Income $100,000 or More
0	060372190203	Block Group 3	Block Group 3, Census Tract 2190.20, Los Angel...	3	323	103	35	107	51	27
1	060372340001	Block Group 1	Block Group 1, Census Tract 2340, Los Angeles ...	1	628	181	149	112	36	150
2	060372340002	Block Group 2	Block Group 2, Census Tract 2340, Los Angeles ...	2	260	14	61	29	11	145
3	060372340003	Block Group 3	Block Group 3, Census Tract 2340, Los Angeles ...	3	299	18	87	25	29	140
4	060372340004	Block Group 4	Block Group 4, Census Tract 2340, Los Angeles ...	4	580	201	32	138	110	99

Clean Up Data for Bar Graphs (Atwater)

df2_sum= df2.sum().reset_index()

df2_sum=df2_sum.set_index('index').T

df2_sum.head()

index	FIPS	Geo_NAME	Geo_QName	Geo_BLKGRP	Total Households in Leimert Park	Household Income Less than $25,000	Household Income $25,000 to $49,999	Household Income $50,000 to $74,999	Household Income $75,000 to $99,999	Household Income $100,000 or More
0	0603721902030603723400010603723400020603723400...	Block Group 3Block Group 1Block Group 2Block G...	Block Group 3, Census Tract 2190.20, Los Angel...	34	5711	1565	1242	1013	550	1341

df2_sum['% Less than $25K'] = df2_sum['Household Income Less than $25,000']/df2_sum['Total Households in Leimert Park']*100

df2_sum['% More than $100K'] = df2_sum['Household Income $100,000 or More']/df2_sum['Total Households in Leimert Park']*100

df2_sum['neighborhood'] = 'Leimert'

df2_sum.plot.bar(x = 'neighborhood', y='% Less than $25K', title="Percentage of Households with Income Less than $25K (2018)")

<matplotlib.axes._subplots.AxesSubplot at 0x7fa82b1df670>

df2_sum.plot.bar(x = 'neighborhood', y='% More than $100K', title="Percentage of Households with Income More than $100K (2018)")

<matplotlib.axes._subplots.AxesSubplot at 0x7fa82b1bf400>

Normalize Data for Mapping

df2['neighborhood'] = 'Leimert'

df2['p_less_25k'] = df2['Household Income Less than $25,000']/df2['Total Households in Leimert Park']*100

df2['p_more_100k'] = df2['Household Income $100,000 or More']/df2['Total Households in Leimert Park']*100

df2['Total Households'] = df2['Total Households in Leimert Park']

df2.head()

	FIPS	Geo_NAME	Geo_QName	Geo_BLKGRP	Total Households in Leimert Park	Household Income Less than $25,000	Household Income $25,000 to $49,999	Household Income $50,000 to $74,999	Household Income $75,000 to $99,999	Household Income $100,000 or More	neighborhood	p_less_25k	p_more_100k	Total Households
0	060372190203	Block Group 3	Block Group 3, Census Tract 2190.20, Los Angel...	3	323	103	35	107	51	27	Leimert	31.888545	8.359133	323
1	060372340001	Block Group 1	Block Group 1, Census Tract 2340, Los Angeles ...	1	628	181	149	112	36	150	Leimert	28.821656	23.885350	628
2	060372340002	Block Group 2	Block Group 2, Census Tract 2340, Los Angeles ...	2	260	14	61	29	11	145	Leimert	5.384615	55.769231	260
3	060372340003	Block Group 3	Block Group 3, Census Tract 2340, Los Angeles ...	3	299	18	87	25	29	140	Leimert	6.020067	46.822742	299
4	060372340004	Block Group 4	Block Group 4, Census Tract 2340, Los Angeles ...	4	580	201	32	138	110	99	Leimert	34.655172	17.068966	580

px.bar(df2, x="neighborhood", y=["Household Income Less than $25,000",
 "Household Income $25,000 to $49,999",
 "Household Income $50,000 to $74,999",
 "Household Income $75,000 to $99,999",
 "Household Income $100,000 or More"], title="Income Breakdown by Block Group in Leimert Park (2017)", 
       labels={'Geo_NAME':'Census Tract Block Group','value':'Total Households in Leimert Park', 'variable': 'Household Income'})

Merge Atwater Village + Leimert Park Income Data

income_merged= df_sum.append(df2_sum)

income_merged

	FIPS	Geo_NAME	Geo_QName	Geo_BLKGRP	Total Households in Atwater Village	Household Income Less than $25,000	Household Income $25,000 to $49,999	Household Income $50,000 to $74,999	Household Income $75,000 to $99,999	Household Income $100,000 or More	% Less than $25K	% More than $100K	neighborhood	year	Total Households in Leimert Park
0	0603718630110603718630120603718630130603718630...	Block Group 1Block Group 2Block Group 3Block G...	Block Group 1, Census Tract 1863.01, Los Angel...	31	8801	1982	1644	1413	1028	2734	22.5202	31.0647	Atwater	2018	NaN
0	0603721902030603723400010603723400020603723400...	Block Group 3Block Group 1Block Group 2Block G...	Block Group 3, Census Tract 2190.20, Los Angel...	34	NaN	1565	1242	1013	550	1341	27.4033	23.481	Leimert	NaN	5711

income_merged.plot.bar(x = 'neighborhood', y='% Less than $25K', title="Percentage of Households with Income Less than $25K (2018)")

<matplotlib.axes._subplots.AxesSubplot at 0x7fa828e4aa60>

income_merged.plot.bar(x = 'neighborhood', y='% More than $100K', title="Percentage of Households with Income More than $100K (2018)")

<matplotlib.axes._subplots.AxesSubplot at 0x7fa828e05460>

RACE

df3 = pd.read_csv('data/Atwater2018_Race.csv')

df3 = pd.read_csv(
    'data/Atwater2017_Race.csv',
    dtype=
    {
        'Geo_FIPS':str,
        'Geo_STATE':str,
        'Geo_COUNTY': str
    }
)

df3.columns[df3.isna().all()].tolist()

['Geo_US',
 'Geo_REGION',
 'Geo_DIVISION',
 'Geo_STATECE',
 'Geo_COUSUB',
 'Geo_PLACE',
 'Geo_PLACESE',
 'Geo_CONCIT',
 'Geo_AIANHH',
 'Geo_AIANHHFP',
 'Geo_AIHHTLI',
 'Geo_AITSCE',
 'Geo_AITS',
 'Geo_ANRC',
 'Geo_CBSA',
 'Geo_CSA',
 'Geo_METDIV',
 'Geo_MACC',
 'Geo_MEMI',
 'Geo_NECTA',
 'Geo_CNECTA',
 'Geo_NECTADIV',
 'Geo_UA',
 'Geo_UACP',
 'Geo_CDCURR',
 'Geo_SLDU',
 'Geo_SLDL',
 'Geo_VTD',
 'Geo_ZCTA3',
 'Geo_ZCTA5',
 'Geo_SUBMCD',
 'Geo_SDELM',
 'Geo_SDSEC',
 'Geo_SDUNI',
 'Geo_UR',
 'Geo_PCI',
 'Geo_TAZ',
 'Geo_UGA',
 'Geo_BTTR',
 'Geo_BTBG',
 'Geo_PUMA5',
 'Geo_PUMA1']

df3 = df3.dropna(axis=1,how="all")

columns_to_drop = ['Geo_GEOID','Geo_STUSAB','Geo_SUMLEV','Geo_GEOCOMP','Geo_FILEID','Geo_LOGRECNO','Geo_STATE', 'Geo_COUNTY','SE_A04001_002','SE_A04001_011','SE_A04001_012','SE_A04001_013','SE_A04001_014','SE_A04001_015','SE_A04001_016','SE_A04001_017']

df3 = df3.drop(columns_to_drop,axis=1)
df3.head()

	Geo_FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	SE_A04001_001	SE_A04001_003	SE_A04001_004	SE_A04001_005	SE_A04001_006	SE_A04001_007	SE_A04001_008	SE_A04001_009	SE_A04001_010
0	060371863011	Block Group 1, Census Tract 1863.01, Los Angel...	Block Group 1, Census Tract 1863.01, Los Angel...	186301	1	881	322	53	0	78	0	0	44	384
1	060371863012	Block Group 2, Census Tract 1863.01, Los Angel...	Block Group 2, Census Tract 1863.01, Los Angel...	186301	2	1349	103	23	0	89	0	0	6	1128
2	060371863013	Block Group 3, Census Tract 1863.01, Los Angel...	Block Group 3, Census Tract 1863.01, Los Angel...	186301	3	889	59	25	0	196	0	0	4	605
3	060371863021	Block Group 1, Census Tract 1863.02, Los Angel...	Block Group 1, Census Tract 1863.02, Los Angel...	186302	1	1573	566	26	0	409	0	0	84	488
4	060371864011	Block Group 1, Census Tract 1864.01, Los Angel...	Block Group 1, Census Tract 1864.01, Los Angel...	186401	1	2676	78	0	0	312	0	0	7	2279

columns3 = list(df3) # this is the same as df.columns.to_list()
columns3

['Geo_FIPS',
 'Geo_NAME',
 'Geo_QName',
 'Geo_TRACT',
 'Geo_BLKGRP',
 'SE_A04001_001',
 'SE_A04001_003',
 'SE_A04001_004',
 'SE_A04001_005',
 'SE_A04001_006',
 'SE_A04001_007',
 'SE_A04001_008',
 'SE_A04001_009',
 'SE_A04001_010']

##RENAMED TO INCLUDE PROPER TITLES 

df3.columns = ['FIPS',
 'Geo_NAME',
 'Geo_QName',
 'Geo_TRACT',
 'Geo_BLKGRP',
 'Total Population',
 'White',
 'Black',
 'AmIndian',
'Native Hawaiian and Other Pacific Islander Alone',
 'Asian',
 'Some Other Race Alone',
 'Two or More Races',
 'Hispanic or Latino']

df3.head()

	FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	Total Population	White	Black	AmIndian	Native Hawaiian and Other Pacific Islander Alone	Asian	Some Other Race Alone	Two or More Races	Hispanic or Latino
0	060371863011	Block Group 1, Census Tract 1863.01, Los Angel...	Block Group 1, Census Tract 1863.01, Los Angel...	186301	1	881	322	53	0	78	0	0	44	384
1	060371863012	Block Group 2, Census Tract 1863.01, Los Angel...	Block Group 2, Census Tract 1863.01, Los Angel...	186301	2	1349	103	23	0	89	0	0	6	1128
2	060371863013	Block Group 3, Census Tract 1863.01, Los Angel...	Block Group 3, Census Tract 1863.01, Los Angel...	186301	3	889	59	25	0	196	0	0	4	605
3	060371863021	Block Group 1, Census Tract 1863.02, Los Angel...	Block Group 1, Census Tract 1863.02, Los Angel...	186302	1	1573	566	26	0	409	0	0	84	488
4	060371864011	Block Group 1, Census Tract 1864.01, Los Angel...	Block Group 1, Census Tract 1864.01, Los Angel...	186401	1	2676	78	0	0	312	0	0	7	2279

df3_sum= df3.sum().reset_index()

df3_sum

	index	0
0	FIPS	0603718630110603718630120603718630130603718630...
1	Geo_NAME	Block Group 1, Census Tract 1863.01, Los Angel...
2	Geo_QName	Block Group 1, Census Tract 1863.01, Los Angel...
3	Geo_TRACT	3368419
4	Geo_BLKGRP	31
5	Total Population	25387
6	White	5639
7	Black	270
8	AmIndian	157
9	Native Hawaiian and Other Pacific Islander Alone	4663
10	Asian	66
11	Some Other Race Alone	48
12	Two or More Races	721
13	Hispanic or Latino	13823

df3_sum = df3_sum.set_index('index').T

type(df3_sum)

pandas.core.frame.DataFrame

df3_sum.head()

index	FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	Total Population	White	Black	AmIndian	Native Hawaiian and Other Pacific Islander Alone	Asian	Some Other Race Alone	Two or More Races	Hispanic or Latino
0	0603718630110603718630120603718630130603718630...	Block Group 1, Census Tract 1863.01, Los Angel...	Block Group 1, Census Tract 1863.01, Los Angel...	3368419	31	25387	5639	270	157	4663	66	48	721	13823

df3_sum['% Latino'] = df3_sum['Hispanic or Latino']/df3_sum['Total Population']*100

df3_sum['% White'] = df3_sum['White']/df3_sum['Total Population']*100

df3_sum['% Black'] = df3_sum['Black']/df3_sum['Total Population']*100

df3_sum['neighborhood'] = 'Atwater'
df3_sum['year'] = '2018'

df3_sum.head()

index	FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	Total Population	White	Black	AmIndian	Native Hawaiian and Other Pacific Islander Alone	Asian	Some Other Race Alone	Two or More Races	Hispanic or Latino	% Latino	% White	% Black	neighborhood	year
0	0603718630110603718630120603718630130603718630...	Block Group 1, Census Tract 1863.01, Los Angel...	Block Group 1, Census Tract 1863.01, Los Angel...	3368419	31	25387	5639	270	157	4663	66	48	721	13823	54.4491	22.2122	1.06354	Atwater	2018

Race Demographics in Leimert Park

df4 = pd.read_csv('data/Leimert2018_Race.csv')

df4 = pd.read_csv(
    'data/Leimert2018_Race.csv',
    dtype=
    {
        'Geo_FIPS':str,
        'Geo_STATE':str,
        'Geo_COUNTY': str
    }
)

df4.columns[df4.isna().all()].tolist()

['Geo_US',
 'Geo_REGION',
 'Geo_DIVISION',
 'Geo_STATECE',
 'Geo_COUSUB',
 'Geo_PLACE',
 'Geo_PLACESE',
 'Geo_CONCIT',
 'Geo_AIANHH',
 'Geo_AIANHHFP',
 'Geo_AIHHTLI',
 'Geo_AITSCE',
 'Geo_AITS',
 'Geo_ANRC',
 'Geo_CBSA',
 'Geo_CSA',
 'Geo_METDIV',
 'Geo_MACC',
 'Geo_MEMI',
 'Geo_NECTA',
 'Geo_CNECTA',
 'Geo_NECTADIV',
 'Geo_UA',
 'Geo_UACP',
 'Geo_CDCURR',
 'Geo_SLDU',
 'Geo_SLDL',
 'Geo_VTD',
 'Geo_ZCTA3',
 'Geo_ZCTA5',
 'Geo_SUBMCD',
 'Geo_SDELM',
 'Geo_SDSEC',
 'Geo_SDUNI',
 'Geo_UR',
 'Geo_PCI',
 'Geo_TAZ',
 'Geo_UGA',
 'Geo_BTTR',
 'Geo_BTBG',
 'Geo_PUMA5',
 'Geo_PUMA1']

df4 = df4.dropna(axis=1,how="all")

columns_to_drop = ['Geo_GEOID','Geo_STUSAB','Geo_SUMLEV','Geo_GEOCOMP','Geo_FILEID','Geo_LOGRECNO','Geo_STATE', 'Geo_COUNTY','SE_A04001_002','SE_A04001_011','SE_A04001_012','SE_A04001_013','SE_A04001_014','SE_A04001_015','SE_A04001_016','SE_A04001_017']

df4 = df4.drop(columns_to_drop,axis=1)
df4.head()

	Geo_FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	SE_A04001_001	SE_A04001_003	SE_A04001_004	SE_A04001_005	SE_A04001_006	SE_A04001_007	SE_A04001_008	SE_A04001_009	SE_A04001_010
0	060372190203	Block Group 3	Block Group 3, Census Tract 2190.20, Los Angel...	219020	3	1078	8	424	0	28	0	0	0	618
1	060372340001	Block Group 1	Block Group 1, Census Tract 2340, Los Angeles ...	234000	1	1890	14	727	0	0	0	0	36	1113
2	060372340002	Block Group 2	Block Group 2, Census Tract 2340, Los Angeles ...	234000	2	612	98	389	0	10	0	0	0	115
3	060372340003	Block Group 3	Block Group 3, Census Tract 2340, Los Angeles ...	234000	3	781	88	618	0	20	0	0	27	28
4	060372340004	Block Group 4	Block Group 4, Census Tract 2340, Los Angeles ...	234000	4	1307	98	898	0	5	0	0	44	262

columns4 = list(df4) # this is the same as df.columns.to_list()
columns4

['Geo_FIPS',
 'Geo_NAME',
 'Geo_QName',
 'Geo_TRACT',
 'Geo_BLKGRP',
 'SE_A04001_001',
 'SE_A04001_003',
 'SE_A04001_004',
 'SE_A04001_005',
 'SE_A04001_006',
 'SE_A04001_007',
 'SE_A04001_008',
 'SE_A04001_009',
 'SE_A04001_010']

df4.columns = ['FIPS',
 'Geo_NAME',
 'Geo_QName',
 'Geo_TRACT',
 'Geo_BLKGRP',
 'Total Population',
 'White',
 'Black',
 'AmIndian',
'Native Hawaiian and Other Pacific Islander Alone',
 'Asian',
 'Some Other Race Alone',
 'Two or More Races',
 'Hispanic or Latino']

df4['Total Population'].head()

0    1078
1    1890
2     612
3     781
4    1307
Name: Total Population, dtype: int64

df4.head()

	FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	Total Population	White	Black	AmIndian	Native Hawaiian and Other Pacific Islander Alone	Asian	Some Other Race Alone	Two or More Races	Hispanic or Latino
0	060372190203	Block Group 3	Block Group 3, Census Tract 2190.20, Los Angel...	219020	3	1078	8	424	0	28	0	0	0	618
1	060372340001	Block Group 1	Block Group 1, Census Tract 2340, Los Angeles ...	234000	1	1890	14	727	0	0	0	0	36	1113
2	060372340002	Block Group 2	Block Group 2, Census Tract 2340, Los Angeles ...	234000	2	612	98	389	0	10	0	0	0	115
3	060372340003	Block Group 3	Block Group 3, Census Tract 2340, Los Angeles ...	234000	3	781	88	618	0	20	0	0	27	28
4	060372340004	Block Group 4	Block Group 4, Census Tract 2340, Los Angeles ...	234000	4	1307	98	898	0	5	0	0	44	262

df4_sum= df4.sum().reset_index()

df4_sum=df4_sum.set_index('index').T

type(df4_sum)

pandas.core.frame.DataFrame

df4_sum.head()

index	FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	Total Population	White	Black	AmIndian	Native Hawaiian and Other Pacific Islander Alone	Asian	Some Other Race Alone	Two or More Races	Hispanic or Latino
0	0603721902030603723400010603723400020603723400...	Block Group 3Block Group 1Block Group 2Block G...	Block Group 3, Census Tract 2190.20, Los Angel...	3028820	34	12535	713	8015	0	401	8	11	392	2995

df4_sum['% Black'] = (df4_sum['Black']/df4_sum['Total Population'])*100

df4_sum['% White'] = (df4_sum['White']/df4_sum['Total Population'])*100

df4_sum['% Latino'] = (df4_sum['Hispanic or Latino']/df4_sum['Total Population'])*100

df4_sum.head()

index	FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	Total Population	White	Black	AmIndian	Native Hawaiian and Other Pacific Islander Alone	Asian	Some Other Race Alone	Two or More Races	Hispanic or Latino	% Black	% White	% Latino
0	0603721902030603723400010603723400020603723400...	Block Group 3Block Group 1Block Group 2Block G...	Block Group 3, Census Tract 2190.20, Los Angel...	3028820	34	12535	713	8015	0	401	8	11	392	2995	63.941	5.68807	23.8931

df4_sum['neighborhood'] = 'Leimert'
df4_sum['year'] = '2017'

df4_sum

index	FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	Total Population	White	Black	AmIndian	Native Hawaiian and Other Pacific Islander Alone	Asian	Some Other Race Alone	Two or More Races	Hispanic or Latino	% Black	% White	% Latino	neighborhood	year
0	0603721902030603723400010603723400020603723400...	Block Group 3Block Group 1Block Group 2Block G...	Block Group 3, Census Tract 2190.20, Los Angel...	3028820	34	12535	713	8015	0	401	8	11	392	2995	63.941	5.68807	23.8931	Leimert	2017

race_merged= df3_sum.append(df4_sum)

race_merged

	FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	Total Population	White	Black	AmIndian	Native Hawaiian and Other Pacific Islander Alone	Asian	Some Other Race Alone	Two or More Races	Hispanic or Latino	% Latino	% White	% Black	neighborhood	year
0	0603718630110603718630120603718630130603718630...	Block Group 1, Census Tract 1863.01, Los Angel...	Block Group 1, Census Tract 1863.01, Los Angel...	3368419	31	25387	5639	270	157	4663	66	48	721	13823	54.4491	22.2122	1.06354	Atwater	2018
0	0603721902030603723400010603723400020603723400...	Block Group 3Block Group 1Block Group 2Block G...	Block Group 3, Census Tract 2190.20, Los Angel...	3028820	34	12535	713	8015	0	401	8	11	392	2995	23.8931	5.68807	63.941	Leimert	2017

race_merged.plot.bar(x = 'neighborhood', y='% White', title="Percentage of White Population (2018)")

<matplotlib.axes._subplots.AxesSubplot at 0x7fa828dad490>

race_merged.plot.bar(x = 'neighborhood', y='% Black', title="Percentage of Black Population (2018)")

<matplotlib.axes._subplots.AxesSubplot at 0x7fa828cf5250>

race_merged.plot.bar(x = 'neighborhood', y='% Latino', title="Percentage of Latino Population (2018)")

<matplotlib.axes._subplots.AxesSubplot at 0x7fa828cc9e20>

Normalize the Data for Mapping

# create a new column and normalize
# also repeat this for 'p_White'
df3['neighborhood'] = 'Atwater'

df3['p_Latino'] = df3['Hispanic or Latino']/df3['Total Population']*100

df3['p_White'] = df3['White']/df3['Total Population']*100

df3['p_Black'] = df3['Black']/df3['Total Population']*100

df4['neighborhood'] = 'Leimert'

df4['p_Black'] = df4['Black']/df4['Total Population']*100

df4['p_White'] = df4['White']/df4['Total Population']*100

df4['p_Latino'] = df4['Hispanic or Latino']/df4['Total Population']*100

df3.head()

	FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	Total Population	White	Black	AmIndian	Native Hawaiian and Other Pacific Islander Alone	Asian	Some Other Race Alone	Two or More Races	Hispanic or Latino	neighborhood	p_Latino	p_White	p_Black
0	060371863011	Block Group 1, Census Tract 1863.01, Los Angel...	Block Group 1, Census Tract 1863.01, Los Angel...	186301	1	881	322	53	0	78	0	0	44	384	Atwater	43.586833	36.549376	6.015891
1	060371863012	Block Group 2, Census Tract 1863.01, Los Angel...	Block Group 2, Census Tract 1863.01, Los Angel...	186301	2	1349	103	23	0	89	0	0	6	1128	Atwater	83.617494	7.635285	1.704967
2	060371863013	Block Group 3, Census Tract 1863.01, Los Angel...	Block Group 3, Census Tract 1863.01, Los Angel...	186301	3	889	59	25	0	196	0	0	4	605	Atwater	68.053993	6.636670	2.812148
3	060371863021	Block Group 1, Census Tract 1863.02, Los Angel...	Block Group 1, Census Tract 1863.02, Los Angel...	186302	1	1573	566	26	0	409	0	0	84	488	Atwater	31.023522	35.982200	1.652893
4	060371864011	Block Group 1, Census Tract 1864.01, Los Angel...	Block Group 1, Census Tract 1864.01, Los Angel...	186401	1	2676	78	0	0	312	0	0	7	2279	Atwater	85.164425	2.914798	0.000000

df3.head()

	FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	Total Population	White	Black	AmIndian	Native Hawaiian and Other Pacific Islander Alone	Asian	Some Other Race Alone	Two or More Races	Hispanic or Latino	neighborhood	p_Latino	p_White	p_Black
0	060371863011	Block Group 1, Census Tract 1863.01, Los Angel...	Block Group 1, Census Tract 1863.01, Los Angel...	186301	1	881	322	53	0	78	0	0	44	384	Atwater	43.586833	36.549376	6.015891
1	060371863012	Block Group 2, Census Tract 1863.01, Los Angel...	Block Group 2, Census Tract 1863.01, Los Angel...	186301	2	1349	103	23	0	89	0	0	6	1128	Atwater	83.617494	7.635285	1.704967
2	060371863013	Block Group 3, Census Tract 1863.01, Los Angel...	Block Group 3, Census Tract 1863.01, Los Angel...	186301	3	889	59	25	0	196	0	0	4	605	Atwater	68.053993	6.636670	2.812148
3	060371863021	Block Group 1, Census Tract 1863.02, Los Angel...	Block Group 1, Census Tract 1863.02, Los Angel...	186302	1	1573	566	26	0	409	0	0	84	488	Atwater	31.023522	35.982200	1.652893
4	060371864011	Block Group 1, Census Tract 1864.01, Los Angel...	Block Group 1, Census Tract 1864.01, Los Angel...	186401	1	2676	78	0	0	312	0	0	7	2279	Atwater	85.164425	2.914798	0.000000

df3_sum= df3.sum().reset_index()

df3_sum=df3_sum.set_index('index').T

type(df3_sum)

pandas.core.frame.DataFrame

df3_sum['year'] = '2018'

df3_sum

index	FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	Total Population	White	Black	AmIndian	Native Hawaiian and Other Pacific Islander Alone	Asian	Some Other Race Alone	Two or More Races	Hispanic or Latino	neighborhood	p_Latino	p_White	p_Black	year
0	0603718630110603718630120603718630130603718630...	Block Group 1, Census Tract 1863.01, Los Angel...	Block Group 1, Census Tract 1863.01, Los Angel...	3368419	31	25387	5639	270	157	4663	66	48	721	13823	AtwaterAtwaterAtwaterAtwaterAtwaterAtwaterAtwa...	948.999	425.794	22.0826	2018

df3_sum['neighborhood'] = 'Atwater'

df3_sum

index	FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	Total Population	White	Black	AmIndian	Native Hawaiian and Other Pacific Islander Alone	Asian	Some Other Race Alone	Two or More Races	Hispanic or Latino	neighborhood	p_Latino	p_White	p_Black	year
0	0603718630110603718630120603718630130603718630...	Block Group 1, Census Tract 1863.01, Los Angel...	Block Group 1, Census Tract 1863.01, Los Angel...	3368419	31	25387	5639	270	157	4663	66	48	721	13823	Atwater	948.999	425.794	22.0826	2018

df4_sum= df4.sum().reset_index()

df4_sum=df4_sum.set_index('index').T

type(df4_sum)

pandas.core.frame.DataFrame

df4_sum['neighborhood'] = 'Leimert'
df4_sum['year'] = '2018'

df4_sum

index	FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	Total Population	White	Black	AmIndian	Native Hawaiian and Other Pacific Islander Alone	Asian	Some Other Race Alone	Two or More Races	Hispanic or Latino	neighborhood	p_Black	p_White	p_Latino	year
0	0603721902030603723400010603723400020603723400...	Block Group 3Block Group 1Block Group 2Block G...	Block Group 3, Census Tract 2190.20, Los Angel...	3028820	34	12535	713	8015	0	401	8	11	392	2995	Leimert	857.457	81.1305	275.153	2018

df3['Total Households'] = 'Total Households in Atwater Village'

df4['Total Households'] = 'Total Households in Leimert Park'

race_merged= df3_sum.append(df4_sum)

race_merged

	FIPS	Geo_NAME	Geo_QName	Geo_TRACT	Geo_BLKGRP	Total Population	White	Black	AmIndian	Native Hawaiian and Other Pacific Islander Alone	Asian	Some Other Race Alone	Two or More Races	Hispanic or Latino	neighborhood	p_Latino	p_White	p_Black	year
0	0603718630110603718630120603718630130603718630...	Block Group 1, Census Tract 1863.01, Los Angel...	Block Group 1, Census Tract 1863.01, Los Angel...	3368419	31	25387	5639	270	157	4663	66	48	721	13823	Atwater	948.999	425.794	22.0826	2018
0	0603721902030603723400010603723400020603723400...	Block Group 3Block Group 1Block Group 2Block G...	Block Group 3, Census Tract 2190.20, Los Angel...	3028820	34	12535	713	8015	0	401	8	11	392	2995	Leimert	275.153	81.1305	857.457	2018

race_merged.plot.bar(x = 'neighborhood', y='p_White', title="Percentage of White Population (2018)")

<matplotlib.axes._subplots.AxesSubplot at 0x7fa828c893d0>

race_merged.plot.bar(x = 'neighborhood', y='p_Black', title="Percentage of Black Population (2018)")

<matplotlib.axes._subplots.AxesSubplot at 0x7fa828c02640>

race_merged.plot.bar(x = 'neighborhood', y='p_Latino', title="Percentage of Latino Population (2018)")

<matplotlib.axes._subplots.AxesSubplot at 0x7fa8547b4430>

import plotly.express as px
import pandas as pd

Mapping

import geopandas as gpd
block_groups= gpd.read_file('data/tl_2019_06_bg.shp')

# subset to only LA block groups
block_groups_LA = block_groups[block_groups.COUNTYFP == '037']

block_groups_LA.shape

(6425, 13)

columns = list(block_groups)
columns

['STATEFP',
 'COUNTYFP',
 'TRACTCE',
 'BLKGRPCE',
 'GEOID',
 'NAMELSAD',
 'MTFCC',
 'FUNCSTAT',
 'ALAND',
 'AWATER',
 'INTPTLAT',
 'INTPTLON',
 'geometry']

blocks = block_groups_LA[['GEOID','geometry']]
blocks.head()

	GEOID	geometry
30	060371872002	POLYGON ((-118.25704 34.10773, -118.25664 34.1...
31	060371873001	POLYGON ((-118.26544 34.11216, -118.26540 34.1...
32	060371873002	POLYGON ((-118.25924 34.09416, -118.25924 34.0...
36	060375437024	POLYGON ((-118.27553 33.80936, -118.27553 33.8...
37	060375437025	POLYGON ((-118.27549 33.81246, -118.27549 33.8...

blocks.columns = ['FIPS','geometry']

# create a new dataframe based on the join (ATWATER)
blocks_units=blocks.merge(df,on="FIPS")

# subsetting by neighborhood
axA1=blocks_units[blocks_units.neighborhood=='Atwater'].plot(figsize=(12,10),
                 column='p_less_25k',                                  
                  cmap='cool',
                 legend=True,            
                scheme='equal_interval')

# get rid of the axis
axA1.axis('off')

# add a title
axA1.set_title('Census Block Groups in Atwater Village with Annual Household Income Less than $25K (2018)')

Text(0.5, 1.0, 'Census Block Groups in Atwater Village with Annual Household Income Less than $25K (2018)')

# create a new dataframe based on the join (Leimert)
blocks_units2=blocks.merge(df2,on="FIPS")

# subsetting by neighborhood
axL1= blocks_units2[blocks_units2.neighborhood=='Leimert'].plot(figsize=(12,10),
                 column='p_less_25k',                                  
                 cmap='cool',
                 legend=True,
                scheme='equal_interval')
# get rid of the axis
axL1.axis('off')
# add a title
axL1.set_title('Census Block Groups in Leimert Park with Annual Household Income Less than $25K (2018)')

Text(0.5, 1.0, 'Census Block Groups in Leimert Park with Annual Household Income Less than $25K (2018)')

# subsetting by neighborhood
axA2=blocks_units[blocks_units.neighborhood=='Atwater'].plot(figsize=(12,10),
                 column='p_more_100k',                                  
                  cmap='cool',
                 legend=True,
                                
                scheme='equal_interval')

# get rid of the axis
axA2.axis('off')
# add a title
axA2.set_title('Census Block Groups in Atwater Village with Annual Household Income More than $100K (2018)')

Text(0.5, 1.0, 'Census Block Groups in Atwater Village with Annual Household Income More than $100K (2018)')

# subsetting by neighborhood
axL2 = blocks_units2[blocks_units2.neighborhood=='Leimert'].plot(figsize=(12,10),
                 column='p_more_100k',                                  
                  cmap='cool',
                 legend=True,          
                scheme='equal_interval')

# get rid of the axis
axL2.axis('off')

# add a title
axL2.set_title('Census Block Groups in Leimert Park with Annual Household Income More than $100K (2018)')

Text(0.5, 1.0, 'Census Block Groups in Leimert Park with Annual Household Income More than $100K (2018)')